Case-based reasoning as a cloud service

ABSTRACT

The disclosure generally describes methods, software, and systems for providing solution descriptions. A problem description of a problem is received, from a client, at a cloud-based reasoning service. A solution description for a solution to the problem is received. Case metadata for a case defining the problem and solution are generated by the cloud-based reasoning service. The case metadata, including the problem description and solution description, are stored by the cloud-based reasoning service in a cases repository associating solutions with problems. A new problem is received at the cloud-based reasoning service. An automated analysis of the new problem is performed, and a comparison is made of the new problem with existing solutions in the cases repository to identify solutions matching the new problem. A new solution description is provided that is based on a match between the new problem description and the problem description and using the problem solution.

BACKGROUND

The present disclosure relates to enterprise solutions. For example, ateam that implements an intelligent enterprise solution can face severalchallenges and problems. Some modern machine-learning algorithms maydepend on large amounts of data, may require a dedicated trainingenvironment, or may suffer from specific user experience (UX) issues,such as a lack of transparency regarding the data results. Problems suchas these may require complex solutions that may be tailored to specificuse cases. A final design of an enterprise solution may be toocustomized and scalable, which may provide significant disadvantages foran enterprise solution running in the cloud.

SUMMARY

This disclosure generally describes computer-implemented methods,software, and systems for providing a case-based reasoning systemavailable through the cloud. One computer-implemented method includes:receiving, at a case-based reasoning service and from a client, aproblem description of a problem; receiving, at the case-based reasoningservice and from the client, a solution description for a solution tothe problem; generating, by the case-based reasoning service, casemetadata for a case that defines the problem and the solution; storing,by the case-based reasoning service, the problem description, thesolution description, and the case metadata in a cases repositoryassociating solutions with problems; receiving, at the case-basedreasoning service and from the client, a new problem; performing anautomated analysis of the new problem and comparison of the new problemwith existing solutions in the cases repository to identify solutionsmatching the new problem; and providing, by the case-based reasoningservice and to the client, a new solution description based on a matchbetween the new problem description and the problem description andusing the problem solution.

The foregoing and other implementations can each optionally include oneor more of the following features, alone or in combination. Inparticular, one implementation can include all the following features:

In a first aspect, combinable with any of the previous aspects, whereinthe problem description includes one or more of a vector of problemattributes and problem text, and wherein the solution descriptionincludes one or more of a vector of solution attributes and solutiontext.

In a second aspect, combinable with any of the previous aspects, whereinproviding the new solution description includes identifying, from thecases repository, at least one solution associated with at least oneproblem having a problem description matching a new problem descriptionof the new problem.

In a third aspect, combinable with any of the previous aspects, whereinthe case metadata includes case owner information, case creation andmodification dates, and relationships to other cases.

In a fourth aspect, combinable with any of the previous aspects, whereinthe problem description, the solution description, the new problemdescription, and the new solution description are sent through anapplication programming interface (API) used by a client accessing thecase-based reasoning service through the cloud.

In a fifth aspect, combinable with any of the previous aspects, furthercomprising: receiving, at the case-based reasoning service and from theclient, updates to the solution description for the case; and updating,by the case-based reasoning service, the solution description for thecase in the cases repository.

In a sixth aspect, combinable with any of the previous aspects, furthercomprising: receiving, at the case-based reasoning service and from theclient, a share authorization for the case; and updating, by thecase-based reasoning service, the cases repository to make the caseaccessible by other clients.

In a seventh aspect, combinable with any of the previous aspects,further comprising: receiving, at the case-based reasoning service andfrom the client, feedback associated with the new solution description;and updating, by the case-based reasoning service and using thefeedback, statistics about that new solution description.

In an eighth aspect, combinable with any of the previous aspects,further comprising: determining, by the case-based reasoning service andusing the feedback, that the new solution description has not beenselected; identifying, by the case-based reasoning service and using thefeedback, an expert to contact or another solution avenue; andproviding, by the case-based reasoning service and to the client,information identifying the expert to contact or the other solutionavenue.

In a ninth aspect, combinable with any of the previous aspects, furthercomprising: determining, by the case-based reasoning service and usingthe feedback, whether the new solution description is an exact solutionto the problem; and when the new solution description is not an exactsolution to the problem: determining, by the case-based reasoningservice, that a duplicate solution is needed; determining, by thecase-based reasoning service and using the feedback, the duplicatesolution; providing, by the case-based reasoning service and to theclient, a new solution description of the duplicate solution; andupdating, by the case-based reasoning service, the cases repository toinclude the new solution description.

The details of one or more implementations of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an example environment that provides acase-based reasoning service (CBRS).

FIG. 2A is a block diagram of an example environment for using the CBRS.

FIGS. 2B, 2Ba, 2Bb, and 2Bc collectively show a screen shot showingexamples of case vectors.

FIGS. 2C, 2Ca, and 2Cb collectively show a screen shot showing examplesof case vectors and details of a specific case.

FIG. 3 is a block diagram of an example of an entity relationshipdiagram for entities related to cases provided by the CBRS.

FIG. 4 is a block diagram showing example components of a case databasefor collecting and maintaining cases.

FIG. 5 is a block diagram showing example components of interrelatedcase entities.

FIG. 6 is a swim lane diagram showing examples of steps and interactionsin a process for reusing an existing case to provide a solution to aproblem.

FIG. 7 is a swim lane diagram showing examples of steps and interactionsin a process for creating a new case.

FIG. 8 is a swim lane diagram showing examples of steps and interactionsin a process for suggesting an expert when no existing cases haveproblems that match a new problem.

FIG. 9 is a flowchart of an example method for determining solutionsthrough a case-based reasoning service.

FIG. 10 is a block diagram of an exemplary computer system used toprovide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures asdescribed in the instant disclosure.

DETAILED DESCRIPTION

This disclosure generally describes computer-implemented methods,software, and systems for a case-based reasoning service in the cloud.Case-based reasoning (CBR) is a well-known method of computer reasoningin which new problems can be solved based on solutions of similar pastproblems.

In some implementations, a cloud-based CBR service (CBRS) can providecomplete functionalities of a CBR reasoning cycle. The functionalitiescan include retrieve, reuse, revise, and retain functionalities that areavailable across all enterprise applications. The service can be builton the universal case format and a set of standardized applicationprogramming interfaces (APIs) for CBR functionality.

Approaches for providing the service can combine common advantages ofCBR with a cloud approach, making the functionality available to avariety of enterprise use cases with a very limited or a zero-customfootprint.

The CBRS can provide many advantages over conventional machine-learning(ML) approaches for enterprise applications. For example, transparencycan be realized since a user can understand a suggested solution basedon similar solutions from the past. Incremental learning can permit theCBRS to be used without requiring a large amount of data to start. Thisis because the system can learn case-by-case, and learned knowledge canevolve over time with each use. Another advantage is a social component,as the system can learn not only from the data of multiple users, butfrom information provided by experts.

There are other advantages of running CBR as a cloud-based service forenterprise applications. A universal case format and the use ofapplication programming interfaces can provide applications with accessto standard CBR services. In this way, applications can implement theirown CBR cycle of retrieve, reuse, revise, and retain. A shared casedatabase for the central storage of cases can allow users to share theexpertise not only within one company but also among different expertsin related fields or industries. Companies can also offer their casedatabases as a service. A scalable architecture can include a separatedand de-coupled user interface (UI), a CBR service, and data provisioningservices. The architecture can allow a flexible implementation ondifferent platforms and technology stacks.

This disclosure focuses on the cloud-based aspect of the system. When aproblem or case is presented, a determination can be made as to whichsilo or application the incoming problem or case is related to. Aparticular case can be related, for example, to a specific application,a specific user, or a group of users associated with an application. Thesystem can support one company's use of their application and theirsolutions, or the system can be used for shared partner or industryinformation. The system can access a stored set of case information thatcoordinates to a given user in the cloud to perform an analysis todetermine which solutions and existing cases match and can assist with arequest. In some implementations, tools or modules for the evaluationfor similarity can be a pluggable solution, depending on the type ofinput and the appropriate evaluation for a specific analysis. The systemcan determine solutions at runtime or based on specific instructions forparticular users, user groups, or contexts. A service-orientedarchitecture provided by the system can be scaled dynamically with thenumber of users accessing the service. For example, additionalcontainers (such as Docker containers) can be booted up automaticallyusing orchestrators as user loads increase.

FIG. 1 is a block diagram of an example environment 100 that provides acase-based reasoning service (CBRS) 102. The environment 100 includes auser 104 who can use the CBRS 102 as a service for storing, retrieving,reusing, and updating cases of problems and corresponding solutions. Theuser 104 can use or interact with a UI 106 (for example, associated withone or more applications) that runs on a client 108 (for example, amobile device, laptop computer, or other computing device) to interactwith the CBRS 102. In some implementations, applications that supportthe UI 106 can include APIs for sending and receiving problemdescriptions, solution descriptions, new problem descriptions, and newsolution descriptions for use by the client 108 in sending informationthrough the cloud to the CBRS 102. A network 110 (for example, theInternet) can support communication among the components of theenvironment 100. Interactions among the components of the environment100 are described in more detail with reference to the swim lanediagrams of FIGS. 6-8.

The CBRS 102 includes a server 112 that receives requests initiated bythe user 104 to store, retrieve, reuse, and update cases. The server 112can handle the requests and generate messages (based on the request) tobe sent to other components of the CBRS 102. For example, a similaritymodule 114 that perform one or more algorithms used to determine asimilarity between a user's new or current problem and one or moreexisting problems previously defined in or associated with the CBRS 102.The similarity module 114 can identify similar cases and assign aconfidence level for each case that identifies a percentage, score, orother measure indicating a likelihood that the solution will resolve theuser's problem. The similarity module 114 can provide, for use by theuser 104, uniform resource locators (URLs) that the user 104 can use toprovide feedback.

A decision maker module 116 can make decisions as to whether a solutionwill be reused or some other action will be taken. The decision makermodule 116 can enter into various stages of operation depending, forexample, on whether a solution is going to be reused or replaced.

A problem matcher module 118 can compare the user's problem with theproblem of existing cases and corresponding problems. The problemmatcher module 118 can include functions that are executed to determinewhether a new problem matches an existing problem of a case stored bythe CBRS 102.

A case review module 120 can create new cases or revise existing casesbased on information associated with a new case received from the userand a solution selected by the user. An expert finder module 122 canidentify experts that may be able to assist in the user's problem.Identifying experts may be necessary, for example, if the CBRS 102 isunable to identify a case with a solution that is likely to resolve theuser's problem. In some implementations, cases and solutions are onlyselected if a confidence level of the solutions is above a threshold.Confidence-level thresholds can exist, for example, at the level ofindividual users, groups of users, organizations, or by application.

The CBRS 102 includes a memory 124 that can store a cases repository126. The cases repository 126 includes problem descriptions 128,solution descriptions 130, and cases metadata 132. The problemdescriptions 128 can be used by the problem matcher module 118 tocompare a user's new problem with existing cases and correspondingproblem descriptions 128 and solution descriptions 130. The problemdescriptions 128 can include one or more of a vector of problemattributes and problem text, wherein the solution description 130includes one or more of a vector of solution attributes and solutiontext. The solution descriptions 130 can describe, for each solution to aproblem, automatic and manual operations that are to be taken to resolvea problem. The metadata 132 can track usage of existing cases inresolving new cases. The metadata 132 can include, for example, caseowner information, case creation and modification dates, andrelationships to other cases. In some implementations, information fromthe metadata 132, such as successful case reuse and selection rates, canincrease the chances that a particular existing case will be chosen inresponse to a new problem.

The CBRS 102 includes an interface 134 for receiving inputs (forexample, requests and case information to be stored) and fortransmitting responses generated by the server 112. A processor 136 canexecute instructions of modules and other components of the CBRS 102.

FIG. 2A is a block diagram of an example environment 200 for using theCBRS 102. The environment 200 includes a UI level 202 that communicatesthrough a process level 204 to a data level 206 that includesinformation describing problems and solutions, such as in a casesrepository.

The UI level 202 includes a user 208 (for example, a business user) whocan use a business application (app) UI 210 (for example, on a mobiledevice) to use the CBRS. Other users who use the CBRS can include a keyuser 214 (for example, using a configuration UI 216 to set up the CBRS)and a data scientist 218 (for example, using a training UI 220 allowingthe data scientist 218 to provide training and other expertise).

At the process level 204, a digital assistant 212 can serve as anunderlying application and the user interface for users of the CBRS. Thedigital assistant 212 can access internal models 222 that model andprovide access to cases that relate problems to solutions. The models222 can supply application programming interfaces (APIs) 224 that can beused at the application level to access information related to cases.Information associated with events 226 can be used to persist datachanges together and associate the data changes with an event whichtriggered the data modification, for example, deleteUser orcreateBankAccount. For example, further processing steps that areusually executed by programs automatically can be stored as events. Inan example, a customer can use a chat bot to ask for a room reservation.The chat bot can offer an empty room to the customer. Declining theoffer can cause the creation of a “declineOffer” event, which would leadto different solution proposal than an “acceptOffer” event, based onlogic implemented in the system. Situations 228 can identify cases,including problems and solutions and their relationships. Rules 230 candefine how solutions are mapped to and accessed for particular problems.Rules can be used to make decisions in specific situations, such asbased on an urgency, a priority, costs, or other parameters. Forexample, if Machine X is broken, a loss in revenue per hour can be$100,000, and a rule that is based at least on urgency and cost canindicate an action to “order as soon as possible.” If Machine Y broken,for example, and production of Machine Y is in stock, a rule canindicate an action to “order as cheap as possible.” Machine learning(ML) services 232 can provide resources that perform machine learning,such as to learn over time which existing cases' problems are likely tomatch a new problem presented by the user. The digital assistant 212 canalso access external models 234, such as models that are not stored bythe CBRS but that are available through the cloud.

At the data level 206, applications 236 can include applications thatprovide enterprise resource planning (ERP) solutions and that aresimplified, cloud-based (accessible from mobile devices), and integratedwith a cloud platform (on-premises and in the cloud). An ML trainingenvironment 238 can include information that is generated before thesystem can provide solutions. For example, the ML training environment238 can prepare data, including vectorization, before solutions areprovided. In some implementations, system may need to be trained beforeit can be used. User feedback data 240 can include information receivedfrom users as to which suggested solutions were used for a given problemand can include textual information regarding additional informationsuch as to be used to change existing solutions or create new solutions.Environmental data 242 can include, for example, global positioningsystem (GPS) coordinates, system load information, and noise levelinformation.

FIGS. 2B, 2Ba, 2Bb, and 2Bc collectively show a screen shot showingexamples of case vectors 250 associated with cases used in theenvironment 200. For example, each case vector 250 can include a problemvector 252 and a solution vector 254. The case vectors 250 can eachinclude a problem statement (for example, reason for change, type ofchange) and a human decision that has been taken (for example, thesolution vector 254).

Problem vectors 252 include a reason for change 256 (for example,identifying a quality, cost, or customer issue, whether a bill ofmaterials (BOM) exists, whether a tool is related, and whether workinstruction is included. A type of change 258 can specify whether aninspection characteristic exists, whether certification is present,whether buy-off is present, and standard times are used. A change alert260 can indicate if an alert is issued. An operation affected 262 canindicate whether an operation is up, down, or both during the problem. Adelay 264 can indicate a length of delay caused by the problem, such ashigh, small, or none. An urgency of order 266 can indicate whetherfixing the problem has a high urgency or a low urgency. Solution vectors254 can include an order change 268 (indicating whether an orderrequires a rework, a down system, or no change) and a method 270 (forexample, indicating whether the solution action is automatic or manual).

FIGS. 2C, 2Ca, and 2Cb collectively show a screen shot showing examplesof case vectors 250 and details of a specific case 250 a. Details of thecase 250 a are presented in a case details area 272. A case detailsdescription 274 provides details for the case 250 a, including caseinformation, creation and modification information (by case creators andmodifiers), a case status, and a number of times the case has beenviewed. Case modification information 276 identifies informationregarding modifications to the case. Case recommendation information 278provides recommendations for solving the case. A similar cases area 280identified other related cases and their problems and solutions.

FIG. 3 is a block diagram of an example of an entity relationshipdiagram 300 for entities related to cases provided by the CBRS. At thecenter of the entity relationship diagram 300 is a case-based reasoning(CBR) core 302. The CBR core 302 uses a caching layer 304 for performcaching for the CBRS. The caching layer 304 includes a caching manager306 linked to cached cases 308. The CBR core 302 interfaces with aservice layer 310 that can be provided by applications (for example, thedigital assistant 212) and interfaces with UIs. Communications betweenthe CBR core 302 and the service layer 310 use a data provisioningservice 312 interfacing with a caching proxy 314 that operates anapplication cache 316. The service layer 310 communicates with anapplication layer 318 that includes applications 320 that useapplication storage 322. The CBR core 302 communicates with a publicservice layer 324 that includes CBR public services 326. The publicservice layer 324 communicates with a front-end layer 328 that providesbusiness applications 330.

The CBR core 302 can contain all modules which are required to executethe learning process, including vectorizing the cases. In someimplementations, caching capabilities can be absent, therefore the datafrom which to learn can be requested through hypertext transfer protocol(HTTP) (for example, WebSockets) or remote procedure call (RPC) sockets(for example, transmission control protocol (TCP) sockets) directly fromthe data provisioning service 312. A fetching mechanism can be enhancedin different ways, for example, replacing HTTP with WebSockets for astateful communication channel to receive changes without closing theconnection. In some implementations, a web hook can be used.

The caching manager 306 can be used to enhance performance byfacilitating caching of information. The caching manager 306 can providea flexibility to read cases directly from the cache which than can beused, for example, for vectorization of the data. In someimplementations, the learning phase can be skipped and the vectorizeddata can be fetched directly from the cache which can save additionalprocessing time. Caching of similar cases can also be used to preventadditional processing when a user has already asked for a similar case.In some implementations, storing data in the cache can use librariessuch as Redis.

The public service layer 324 can be used for external access, forexample, by applications. The public service layer 324 can implementedusing a Representational State Transfer (REST) API with Hypermedia asthe Engine of Application State (HATEOAS) and Hardware Abstraction Layer(HAL) to include hypermedia information for easy service exploration. Insome implementations, public service layer 324 can be limited toproviding just read-only (or get) operations. The public service layer324 can provide restricted access, for example, requiringauthentication.

The data provisioning service 312 can be implemented as a simple RESTservice that allows create/read/update/delete (CRUD) operations tocreate, read, update and delete new cases. Data from the cases can thenbe used as basis for learning by the CBR core 302.

The caching proxy 314 can be used to fetch and cache data withoutexercising an extra load on the productive system. The application layer318 can contain basic business application rules.

FIG. 4 is a block diagram showing example components of a case database400 for collecting and maintaining cases. The case database 400 canserve as a cases repository that is used by the CBRS to store solutionsto problems and provide suggestion solutions to new problems. The casesdatabase 400 can include information for problems 402, solutions 404,and case metadata 406. Information for problems 402 can include problemvectors with parameters 408 and a textual description 410.

A case can consist of the following parts. First, a problem descriptioncan store the description of the problem as a vector of attributesor/and as a text. The use of storing problems as both a vector and astext can make problems both human-readable and machine-readable. Second,a solution description can store a solution of the problem as a vectorof attributes and as text (for example, to make solutions bothhuman-readable and machine-readable. Third, metadata can storeinformation about case owner, dates of creation and change, andrelationships to other cases. For example, cases can have a hierarchicalrelationship in a case hierarchy.

In some implementations, case information can be stored using JavaScriptObject Notation (JSON):

{ “_id”: “5a58860e2b7bde004437b850”, “problem:” { “Reason”: “Quality”,“BOM”: “no”, “Tool”: “no”, “WorkInstruction”: “no”,“InspectionCharacteristic”: “yes”, “Certification”: “yes”, “BuyOff”:“no”, “StandardTimes”: “no”, “ChangeAlert”: “yes”, “OperationAffected”:“up”, “Delay”: “high”, “Urgency”: “high” }, “solution”: { “OrderChange”:“no”, “OrderChangeMethod”: “” }, “meta”: { “author”: {“id”: “E05323”,“firstName”: “Jacob”, “lastName”: “Mustermann” }, “creationDate”:“2018-01-20” } }

In some implementations, case information can also be stored usingrelational tables, which can include a limitation of predefined schemafor entities. The example JSON format uses main sections (for each ofthe problem, the solution, and the metadata) and an identification (ID),but does not define the contents of the sections. Other formats can beused implement and format the information and provide access byapplications.

The python API can behave like a proxy which uses the original data andattaches additional information to the response. The additionalinformation can include, for example, information relevant to theconsumer (for example, for use in the application) which may beirrelevant at the core system where cases are handled. As an example, afunction /cases/string:task_id can return an original object and anarray with similar cases, for example:

{ “similarCases”: [ { “case”: { “_id”: “5a58860e2b7bde004437b851”, “ID”:2, “Reason”: “Quality”, “BOM”: “no”, “Tool”: “no”, “WorkInstruction”:“no”, “InspectionCharacteristic”: “yes”, “Certification”: “yes”,“BuyOff”: “no”, “StandardTimes”: “no”, “ChangeAlert”: “yes”,“OperationAffected”: “up”, “Delay”: “high”, “Urgency”: “high”,“OrderChange”: “no”, “OrderChangeMethod”: “” }, “confidenceLevel”: 0.80}, ], “_links”: { “case”: { “href”: “https://cbr-dataprovisioning-service.cfapps.sap.hana.ondemand.com/api/cases/5a58860e2b7bde004437b850”} } }

In some implementations, a JSON can be returned that contains N (forexample, three) cases that are the most similar cases to a given caseID. Each case ID can identify a case in a database and accessed using anAPI.

In some implementations, additional functions can be used. A/getSolutionProposals/string:task_id or task/:task_id/solution function,for example, can return possible solution proposals using a HATEOASpattern. A given solution can be a similar case, an expert contact, or afollow-up question. A /setSolutionProposals/string:task_id can allow anapplication to submit the solution chosen by the user (for example,using the user's feedback). Depending on input from the user (forexample, a choice from cases provided by the user), the service candecide whether to create a new case, reuse an existing case, or updatean existing case. Statistics can be updated based on the user'sselection.

In some implementations, generic APIs can be used for executing genericoperations (for example, CRUD operations) on a specific collection (forexample, a set of cases). To be used with the generic API, thecollection can be defined using a query parameter, for example, usingparameters from the following table:

TABLE 1 Query Parameters HTTP Event Method API URL Get All Objects GEThttp://{host}:{port}/generic?collection={name} Get Object by ID GEThttp://{host}:{port}/generic/{objectId}?collection={name} Delete ObjectDELETE http://{host}:{port}/generic/{objectId}?collection={name} AddObject POST http://{host}:{port}/generic?collection={name}

FIG. 5 is a block diagram showing example components of interrelatedcase entities 500. The entities 500 include a problem matching node 502that can interface with a decision maker node 504 to compare given cases506 in order to identify existing solutions for a given new problem. Ifa solution cannot be identified, the decision maker node 504 can invokean Expert finder node 508 to get an expert 510. If a solution is to beupdated, for example, the decision maker node 504 can invoke a casereviewer node 512 to revise 514 a case.

The decision maker node 504 can invoke a case data access object (DAO)node 516, for example, to fetch cases 518 corresponding to a newproblem. If a case is to be added, then the case reviewer node 512 caninvoke the case DAO node 516 to create a case 520. The case DAO node 516can use a JSON de-serializer node 522 and a JSON serializer node 524 toperform solution de-serializing and serializing, respectively. The nodes522 and 524 can invoke an expert DAO node 526 to obtain expertinformation. The case DAO node 516 and the expert DAO node 526 can use adatabase connection 528. Nodes 502, 504, and 512 are nodes of Reuse andRevise classes. Nodes 516, 522, and 524 can serve as refactoring advicenodes.

FIG. 6 is a swim lane diagram showing examples of operations andinteractions in a process 600 for reusing an existing case to provide asolution to a problem. Operations and interactions of the process 600include interactions among a user 602 (for example, the user 104), a UI604 (for example, the UI 106), a server 606 (for example, the server112), a similarity module 608 (for example, the similarity module 114),a decision maker module 610 (for example, the decision maker module116), and a problem matcher module 612 (for example, the problem matchermodule 118).

When a problem on the user's side occurs, a client on the user's sidecan send a message 614 to the UI 604, informing the UI 604 (for example,a UI of a CBR application, such as a user assistant application) of theproblem. The application can enter or transform into a Retrieve stage,for example using a retrieve function 616 of an API (for example, the<<cbr-python_api>>), to request the most similar cases to the user'sproblem. The UI 604 can send the request to the server 606 using, forexample, /cases/string:task_id in the request. The server 606 canforward the request using a message 618, for example, to call thesimilarity module 608 using a function getSimilarCases(task_id). Thefunction can return a message 620 that includes the three cases that aremost similar to the user's problem and a related confidence level foreach case. The server 606 can return a message 622 that identifies thethree most similar cases and includes URLs for the user 602 to providefeedback. User feedback options can include Match and No-match andwhether a matching case's solution is being Reused or Revised. The UI604 can provide a message 624 to the user 602 that identifies the threemost similar cases and the options included.

If the user 602 decides that the second case of the three cases includesthe solution needed for the user's problem, the user 602 can make aselection 626 for the option match. The UI 604 can generate a message628 to the server 606, using the URL related to reuse of the case. Theserver 606 can send a message 630 to the decision maker module 610, forexample, calling a handleMatch (task_id, matchedCase) function in aDecisionMaker class. The function can be used to decide whether the usertriggered a Revise stage or a Reuse stage. The decision maker module 610can compare the user's problem with the problem of the case and theselected solution. The decision maker module 610 can create and send anew object 632 to the problem matcher module 612. For example, theobject 632 can be a function equals (Case1, Case2). In this example, ifthe user's problem matches the problem of the selected case, then thefunction equals (Case1, Case2) returns TRUE in a message 634.

Because the message 630 (for example, handleMatch (task_id,matchedCase)) results in TRUE, the decision maker module 610 can enter aReuse stage and can provide a message 636 to the server 606 which canadjust the statistics of the selected case. The server 606 can provide amessage 638 to the UI 604. The UI 604 can provide a message 640 to theuser 602. The messages 638 and 640 can serve, ultimately, to inform theuser 602 of the decision (for example, “Case reused. Adjusted Casestatistics.”).

FIG. 7 is a swim lane diagram showing examples of operations andinteractions in a process 700 for creating a new case. Operations andinteractions of the process 700 include interactions with a case reviewmodule 702 (for example, the case review module 120) in addition to theuser 602, the UI 604, the server 606, the similarity module 608, thedecision maker module 610, and the problem matcher module 612 that areused in process 600.

The process 700 can start out the same as the process 600, withoperations 704-714 matching or similar to operations 614-624. Forexample, when a problem on the user's side occurs, a message 704 isgenerated and sent to the UI 604. The message 704 allows the user 602 toinform the UI of the CBR-application about the problem. At this time,the application can enter the Retrieve stage. Using a message 706, theUI 604 can call the retrieve function (for example, using the<<cbr-python_api>>) to get the most similar cases to the user's problem.For example, using the message 706, the UI 604 can call the server 606using/cases/string:task_id. Using a message 708, the server 606 can callthe similarity module 608 with the function, for example,getSimilarCases (task_id). Over a sequence of messages 710, 712, and714, the function can return the three most similar cases to the user'sproblem and the related confidence level. For example, the server 606can return the three most similar cases and some URLs for options,including feedback URLs for the cases of Match versus No-match, andactions of Reuse versus Revise. The UI 604 can provide the three mostsimilar cases and the options included to the user 602.

Operations 716 through 724 can match or are similar to the operations626-634. For example, if the user 602 decides that the second case ofthe three cases includes the solution needed for the user's problem,then the user 602 can make a selection 716 for the option match. The UI604 can generate a message 718 to the server 606, using the URL relatedto reuse of the case. The server 606 can send a message 720 to thedecision maker module 610, for example, calling the handleMatch(task_id, matchedCase) function. The function can be used to decidewhether the user triggered the Revise stage or the Reuse stage. Thedecision maker module 610 can compare the user's problem with theproblem of the case and the selected solution. The decision maker module610 can create and send a new object 722 to the problem matcher module612. For example, the object 722 can be a function equals (Case1,Case2). In this example, if the user's problem does not match theproblem of the selected case, the function equals (Case1, Case2) canreturn FALSE through a message 724.

Because the handleMatch (task_id, matchedCase) receives a FALSE result,the decision maker module 610 can decide to enter the Revise stage. Theproblem matcher module 612 can call the case review module 702 using amessage 726 that includes, for example, the reviseCase (Case,matchedCase) function of the CaseReviser. At the case review module 702,the reviseCase (Case, matchedCase) function can create (728) a new casewith the user's problem and the selected solution. In a message 730 sentto the decision maker module 610, the case review module 702 can returnTRUE when the case has been successfully created. Using a sequence ofmessages 732, 734, and 736, the decision maker module 610 can inform theserver 606 with a “New Case created” status, which can be forwarded tothe UI 604 and the user 602.

FIG. 8 is a swim lane diagram showing examples of operations andinteractions in a process 800 for suggesting an expert when no existingcases have problems that match a new problem. Operations andinteractions of the process 800 include interactions with an expertfinder module 802 (for example, the expert finder module 122) inaddition to the user 602, the UI 604, the server 606, the similaritymodule 608, and the decision maker module 610 that are used in processes600 and 700.

As does process 700, process 800 can also start out the same as process600, with operations 804-814 matching or similar to operations 614-624.For example, when a problem on the user's side occurs, a message 804 isgenerated and sent to the UI 604 which allows the user 602 to inform theUI of the CBR-application about the problem. At this time, theapplication can enter the Retrieve stage. Using a message 806, the UI604 can call the retrieve function (for example, using the<<cbr-python_api>>) to get the most similar cases to the user's problem.For example, using the message 806, the UI 604 can call the server 606using /cases/string:task_id. Using a message 808, the server 606 cancall the similarity module 608 with the function, for example,getSimilarCases (task_id). Over a sequence of messages 810, 812, and814, the function can return the three cases that are most similar tothe user's problem with related confidence levels. For example, theserver 606 can return the three most similar cases and some URLs foroptions, including feedback URLs for the cases of Match versus No-match,and actions of Reuse versus Revise. The UI 604 can provide the threemost similar cases and corresponding feedback options to the user 602.

The user 602 can decide that none of the proposed solutions of the threereturned cases meet the user's needs. The user 602 can send a message816 to this effect to the UI 604. The UI 604 can send a message 818 tothe server 606 which can use a message 820 to call a noMatch (task_id)function of the DecisionMaker class. This function can trigger thefunction getExpertForCase (case) of the ExpertFinder class. Thisfunction can return a list of experts based on the user's problem. Thedecision maker module 610 can send a message 822 to the expert findermodule 802 to request experts that can help with the user's problem.Using a message 824, the expert finder module 802 can provide a list ofexperts to the decision maker module 610. The decision maker module 610can send a message 826 to the server 606 that “Nothing was done” andoptionally “But we have a list of experts.” Using messages 828 and 830,the information can be forwarded back through the UI 604 to the user602.

FIG. 9 is a flowchart of an example method 900 for determining solutionsthrough a case-based reasoning service. Method 900 can be performed bythe CBRS 102, for example. Operations 902 through 908 can be performedrepeatedly for multiple cases and used a learning process. For clarityof presentation, the description that follows generally describes method900 in the context of FIGS. 1-8.

At 902, a problem description of a problem is received, from a client,at a case-based reasoning service. For example, over time the CBRS 102can receive case information for problems that have been handled andsolved by the user 104.

At 904, a solution description for a solution to the problem isreceived, from a client, at the case-based reasoning service. Forexample, the CBRS 102 can also receive case information that includesthe corresponding solutions identified by the user 104.

At 906, case metadata for a case that defines the problem and thesolution are generated by the case-based reasoning service. For example,the CBRS 102 can create and maintain metadata for each of the problemsand solutions received from the user 104. The metadata can be updatedover time as cases and corresponding solutions are reused or otheractions are taken (for example, suggesting an expert).

At 908, the case metadata including the problem description and thesolution description are stored by the case-based reasoning service in acases repository associating solutions with problems. For example, theCBRS 102 can store the case information (problems and solutions) in thecases repository 126.

At 910, a new problem is received, from the client, at the case-basedreasoning service. For example, the CBRS 102 can receive a new problemidentified by the user 104 though the UI 106.

At 912, an automated analysis of the new problem is performed and acomparison is made of the new problem with existing solutions in thecases repository to identify solutions matching or significantly similarto the new problem. As an example, the problem matcher module 118 canidentify a case from the cases repository 126 having a problem thatmatches or is significantly similar to the new problem of the user 104.

At 914, a new solution description is provided by the case-basedreasoning service to the client. The new solution description is basedon a match or similarity between the new problem description and theproblem description. For example, the CBRS 102 can provide the solutionto the client 108 for presentation to the user 104 through the UI 106.

In some implementations, method 900 can further include operations forupdating existing cases stored by the CBRS 102. For example, updates tothe solution description for the case can be received from the client atthe CBRS 102. The CBRS can then update the solution description for thecase in the cases repository 126.

In some implementations, method 900 can further include operations forallowing cases to be shared with other users. For example, a shareauthorization for the case can be received at the CBRS 102 from theclient identifying other users or groups who can use information fromthe cases. The cases repository can then be updated to make the caseaccessible by other clients, for example, to share already persistedcases with other companies. For example, Company A can use a service toshare its data with Company B. From a commercial point-of-view, CompanyA can receive a fee from Company B, where the fee is based on a numberof requests that have been made. In another business model, Company Acan collect data from other companies to improve their own data base.Authentication can be provided using oAuth or different methods. FIG. 10is a block diagram of an exemplary computer system 1000 used to providecomputational functionalities associated with described algorithms,methods, functions, processes, flows, and procedures as described in theinstant disclosure.

The illustrated computer 1002 is intended to encompass any computingdevice such as a server, desktop computer, laptop/notebook computer,wireless data port, smart phone, personal data assistant (PDA), tabletcomputing device, one or more processors within these devices, or anyother suitable processing device, including both physical or virtualinstances (or both) of the computing device. Additionally, the computer1002 may comprise a computer that includes an input device, such as akeypad, keyboard, touch screen, or other device that can accept userinformation, and an output device that conveys information associatedwith the operation of the computer 1002, including digital data, visual,or audio information (or a combination of information), or a graphicaluser interface (GUI).

The computer 1002 can serve in a role as a client, network component, aserver, a database or other persistency, or any other component (or acombination of roles) of a computer system for performing the subjectmatter described in the instant disclosure. The illustrated computer1002 is communicably coupled with a network 1030. In someimplementations, one or more components of the computer 1002 may beconfigured to operate within environments, includingcloud-computing-based, local, global, or other environment (or acombination of environments).

At a high level, the computer 1002 is an electronic computing deviceoperable to receive, transmit, process, store, or manage data andinformation associated with the described subject matter. According tosome implementations, the computer 1002 may also include or becommunicably coupled with an application server, e-mail server, webserver, caching server, streaming data server, business intelligence(BI) server, or other server (or a combination of servers).

The computer 1002 can receive requests over network 1030 from a clientapplication (for example, executing on another computer 1002) andresponding to the received requests by processing the said requests inan appropriate software application. In addition, requests may also besent to the computer 1002 from internal users (for example, from acommand console or by other appropriate access method), external orthird-parties, other automated applications, as well as any otherappropriate entities, individuals, systems, or computers.

Each of the components of the computer 1002 can communicate using asystem bus 1003. In some implementations, any or all of the componentsof the computer 1002, both hardware or software (or a combination ofhardware and software), may interface with each other or the interface1004 (or a combination of both) over the system bus 1003 using an API1012 or a service layer 1013 (or a combination of the API 1012 andservice layer 1013). The API 1012 may include specifications forroutines, data structures, and object classes. The API 1012 may beeither computer-language independent or dependent and refer to acomplete interface, a single function, or even a set of APIs. Theservice layer 1013 provides software services to the computer 1002 orother components (whether or not illustrated) that are communicablycoupled to the computer 1002. The functionality of the computer 1002 maybe accessible for all service consumers using this service layer.Software services, such as those provided by the service layer 1013,provide reusable, defined business functionalities through a definedinterface. For example, the interface may be software written in JAVA,C++, or other suitable language providing data in extensible markuplanguage (XML) format or other suitable format. While illustrated as anintegrated component of the computer 1002, alternative implementationsmay illustrate the API 1012 or the service layer 1013 as stand-alonecomponents in relation to other components of the computer 1002 or othercomponents (whether or not illustrated) that are communicably coupled tothe computer 1002. Moreover, any or all parts of the API 1012 or theservice layer 1013 may be implemented as child or sub-modules of anothersoftware module, enterprise application, or hardware module withoutdeparting from the scope of the instant disclosure.

The computer 1002 includes an interface 1004. Although illustrated as asingle interface 1004 in FIG. 10, two or more interfaces 1004 may beused according to particular needs, desires, or particularimplementations of the computer 1002. The interface 1004 is used by thecomputer 1002 for communicating with other systems in a distributedenvironment that are connected to the network 1030 (whether illustratedor not). Generally, the interface 1004 comprises logic encoded insoftware or hardware (or a combination of software and hardware) andoperable to communicate with the network 1030. More specifically, theinterface 1004 may comprise software supporting one or morecommunication protocols associated with communications such that thenetwork 1030 or interface's hardware is operable to communicate physicalsignals within and outside of the illustrated computer 1002.

The computer 1002 includes a processor 1005. Although illustrated as asingle processor 1005 in FIG. 10, two or more processors may be usedaccording to particular needs, desires, or particular implementations ofthe computer 1002. Generally, the processor 1005 executes instructionsand manipulates data to perform the operations of the computer 1002 andany algorithms, methods, functions, processes, flows, and procedures asdescribed in the instant disclosure.

The computer 1002 also includes a memory 1006 that holds data for thecomputer 1002 or other components (or a combination of both) that can beconnected to the network 1030 (whether illustrated or not). For example,memory 1006 can be a database storing data consistent with thisdisclosure. Although illustrated as a single memory 1006 in FIG. 10, twoor more memories may be used according to particular needs, desires, orparticular implementations of the computer 1002 and the describedfunctionality. While memory 1006 is illustrated as an integral componentof the computer 1002, in alternative implementations, memory 1006 can beexternal to the computer 1002.

The application 1007 is an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 1002, particularly with respect tofunctionality described in this disclosure. For example, application1007 can serve as one or more components, modules, applications, etc.Further, although illustrated as a single application 1007, theapplication 1007 may be implemented as multiple applications 1007 on thecomputer 1002. In addition, although illustrated as integral to thecomputer 1002, in alternative implementations, the application 1007 canbe external to the computer 1002.

There may be any number of computers 1002 associated with, or externalto, a computer system containing computer 1002, each computer 1002communicating over network 1030. Further, the term “client,” “user,” andother appropriate terminology may be used interchangeably as appropriatewithout departing from the scope of this disclosure. Moreover, thisdisclosure contemplates that many users may use one computer 1002, orthat one user may use multiple computers 1002.

In some implementations, components of the environments and systemsdescribed above may be any computer or processing device such as, forexample, a blade server, general-purpose personal computer (PC),Macintosh, workstation, UNIX-based workstation, or any other suitabledevice. In other words, the present disclosure contemplates computersother than general purpose computers, as well as computers withoutconventional operating systems. Further, components may be adapted toexecute any operating system, including Linux, UNIX, Windows, Mac OS®,Java™, Android™, iOS or any other suitable operating system. Accordingto some implementations, components may also include, or be communicablycoupled with, an e-mail server, a web server, a caching server, astreaming data server, and/or other suitable server(s).

Processors used in the environments and systems described above may be acentral processing unit (CPU), an application specific integratedcircuit (ASIC), a field-programmable gate array (FPGA), or anothersuitable component. Generally, each processor can execute instructionsand manipulates data to perform the operations of various components.Specifically, each processor can execute the functionality required tosend requests and/or data to components of the environment and toreceive data from the components of the environment, such as incommunication between the external, intermediary and target devices.

Components, environments and systems described above may include amemory or multiple memories. Memory may include any type of memory ordatabase module and may take the form of volatile and/or non-volatilememory including, without limitation, magnetic media, optical media,random access memory (RAM), read-only memory (ROM), removable media, orany other suitable local or remote memory component. The memory maystore various objects or data, including caches, classes, frameworks,applications, backup data, business objects, jobs, web pages, web pagetemplates, database tables, repositories storing business and/or dynamicinformation, and any other appropriate information including anyparameters, variables, algorithms, instructions, rules, constraints, forreferences thereto associated with the purposes of the target,intermediary and external devices. Other components within the memoryare possible.

Regardless of the particular implementation, “software” may includecomputer-readable instructions, firmware, wired and/or programmedhardware, or any combination thereof on a tangible medium (transitory ornon-transitory, as appropriate) operable when executed to perform atleast the processes and operations described herein. Indeed, eachsoftware component may be fully or partially written or described in anyappropriate computer language including C, C++, Java™, Visual Basic,assembler, Perl®, any suitable version of 4GL, as well as others.Software may instead include a number of sub-modules, third-partyservices, components, libraries, and such, as appropriate. Conversely,the features and functionality of various components can be combinedinto single components as appropriate.

Devices can encompass any computing device such as a smart phone, tabletcomputing device, PDA, desktop computer, laptop/notebook computer,wireless data port, one or more processors within these devices, or anyother suitable processing device. For example, a device may comprise acomputer that includes an input device, such as a keypad, touch screen,or other device that can accept user information, and an output devicethat conveys information associated with components of the environmentsand systems described above, including digital data, visual information,or a GUI. The GUI interfaces with at least a portion of the environmentsand systems described above for any suitable purpose, includinggenerating a visual representation of a web browser.

The preceding figures and accompanying description illustrate exampleprocesses and computer implementable techniques. The environments andsystems described above (or their software or other components) maycontemplate using, implementing, or executing any suitable technique forperforming these and other tasks. It will be understood that theseprocesses are for illustration purposes only and that the described orsimilar techniques may be performed at any appropriate time, includingconcurrently, individually, in parallel, and/or in combination. Inaddition, many of the operations in these processes may take placesimultaneously, concurrently, in parallel, and/or in different ordersthan as shown. Moreover, processes may have additional operations, feweroperations, and/or different operations, so long as the methods remainappropriate.

In other words, although this disclosure has been described in terms ofcertain implementations and generally associated methods, alterationsand permutations of these implementations, and methods will be apparentto those skilled in the art. Accordingly, the above description ofexample implementations does not define or constrain this disclosure.Other changes, substitutions, and alterations are also possible withoutdeparting from the spirit and scope of this disclosure.

What is claimed is:
 1. A computer-implemented method comprising:receiving, at a case-based reasoning service and from a client, aproblem description of a problem; receiving, at the case-based reasoningservice and from the client, a solution description for a solution tothe problem; generating, by the case-based reasoning service, casemetadata for a case that defines the problem and the solution; storing,by the case-based reasoning service, the problem description, thesolution description, and the case metadata in a cases repositoryassociating solutions with problems; receiving, at the case-basedreasoning service and from the client, a new problem; performing anautomated analysis of the new problem and comparison of the new problemwith existing solutions in the cases repository to identify solutionsmatching the new problem; providing, by the case-based reasoning serviceand to the client, a new solution description based on a match betweenthe new problem description and the problem description and using theproblem solution; receiving, at the case-based reasoning service andfrom the client, feedback associated with the new solution description;updating, by the case-based reasoning service and using the feedback,statistics about that new solution description; determining, by thecase-based reasoning service and using the feedback, that the newsolution description has not been selected; identifying, by thecase-based reasoning service and using the feedback, an expert tocontact or another solution avenue; and providing, by the case-basedreasoning service and to the client, information identifying the expertto contact or the other solution avenue.
 2. The computer-implementedmethod of claim 1, wherein the problem description includes one or moreof a vector of problem attributes and problem text, and wherein thesolution description includes one or more of a vector of solutionattributes and solution text.
 3. The computer-implemented method ofclaim 2, wherein providing the new solution description includesidentifying, from the cases repository, at least one solution associatedwith at least one problem having a problem description matching a newproblem description of the new problem.
 4. The computer-implementedmethod of claim 1, wherein the case metadata includes case ownerinformation, case creation and modification dates, and relationships toother cases.
 5. The computer-implemented method of claim 1, wherein theproblem description, the solution description, the new problemdescription, and the new solution description are sent through anapplication programming interface (API) used by a client accessing thecase-based reasoning service through the cloud.
 6. Thecomputer-implemented method of claim 1, further comprising: receiving,at the case-based reasoning service and from the client, updates to thesolution description for the case; and updating, by the case-basedreasoning service, the solution description for the case in the casesrepository.
 7. The computer-implemented method of claim 1, furthercomprising: receiving, at the case-based reasoning service and from theclient, a share authorization for the case; and updating, by thecase-based reasoning service, the cases repository to make the caseaccessible by other clients.
 8. A system comprising: memory storingtables storing cases associating problems and solutions, and metadataidentifying the use of the cases over time; and a server performingoperations comprising: receiving, at a case-based reasoning service andfrom a client, a problem description of a problem; receiving, at thecase-based reasoning service and from the client, a solution descriptionfor a solution to the problem; generating, by the case-based reasoningservice, case metadata for a case that defines the problem and thesolution; storing, by the case-based reasoning service, the problemdescription, the solution description, and the case metadata in a casesrepository associating solutions with problems; receiving, at thecase-based reasoning service and from the client, a new problem;performing an automated analysis of the new problem and comparison ofthe new problem with existing solutions in the cases repository toidentify solutions matching the new problem; providing, by thecase-based reasoning service and to the client, a new solutiondescription based on a match between the new problem description and theproblem description and using the problem solution; receiving, at thecase-based reasoning service and from the client, feedback associatedwith the new solution description; updating, by the case-based reasoningservice and using the feedback, statistics about that new solutiondescription; determining, by the case-based reasoning service and usingthe feedback, that the new solution description has not been selected;identifying, by the case-based reasoning service and using the feedback,an expert to contact or another solution avenue; and providing, by thecase-based reasoning service and to the client, information identifyingthe expert to contact or the other solution avenue.
 9. The system ofclaim 8, wherein the problem description includes one or more of avector of problem attributes and problem text, and wherein the solutiondescription includes one or more of a vector of solution attributes andsolution text.
 10. The system of claim 9, wherein providing the newsolution description includes identifying, from the cases repository, atleast one solution associated with at least one problem having a problemdescription matching a new problem description of the new problem. 11.The system of claim 8, wherein the case metadata includes case ownerinformation, case creation and modification dates, and relationships toother cases.
 12. The system of claim 8, wherein the problem description,the solution description, the new problem description, and the newsolution description are sent through an application programminginterface (API) used by a client accessing the case-based reasoningservice through the cloud.
 13. A non-transitory computer-readable mediaencoded with a computer program, the program comprising instructionsthat when executed by one or more computers cause the one or morecomputers to perform operations comprising: receiving, at a case-basedreasoning service and from a client, a problem description of a problem;receiving, at the case-based reasoning service and from the client, asolution description for a solution to the problem; generating, by thecase-based reasoning service, case metadata for a case that defines theproblem and the solution; storing, by the case-based reasoning service,the problem description, the solution description, and the case metadatain a cases repository associating solutions with problems; receiving, atthe case-based reasoning service and from the client, a new problem;performing an automated analysis of the new problem and comparison ofthe new problem with existing solutions in the cases repository toidentify solutions matching the new problem; providing, by thecase-based reasoning service and to the client, a new solutiondescription based on a match between the new problem description and theproblem description and using the problem solution; receiving, at thecase-based reasoning service and from the client, feedback associatedwith the new solution description; updating, by the case-based reasoningservice and using the feedback, statistics about that new solutiondescription; determining, by the case-based reasoning service and usingthe feedback, that the new solution description has not been selected;identifying, by the case-based reasoning service and using the feedback,an expert to contact or another solution avenue; and providing, by thecase-based reasoning service and to the client, information identifyingthe expert to contact or the other solution avenue.
 14. Thenon-transitory computer-readable media of claim 13, wherein the problemdescription includes one or more of a vector of problem attributes andproblem text, and wherein the solution description includes one or moreof a vector of solution attributes and solution text.
 15. Thenon-transitory computer-readable media of claim 14, wherein providingthe new solution description includes identifying, from the casesrepository, at least one solution associated with at least one problemhaving a problem description matching a new problem description of thenew problem.
 16. The non-transitory computer-readable media of claim 13,wherein the case metadata includes case owner information, case creationand modification dates, and relationships to other cases.
 17. Thenon-transitory computer-readable media of claim 13, wherein the problemdescription, the solution description, the new problem description, andthe new solution description are sent through an application programminginterface (API) used by a client accessing the case-based reasoningservice through the cloud.